As an alternative to assuming a specific form for the underlying stochastic process, [1]} proposes a non-parametric deep model called DeepHit, to model the survival functions for the competing risk events. Since no assumptions are made on the survival distribution, the relationship between covariates and the event(s) can now change over time. This is considered an advantage of DeepHit over CPH-based models. The first part of the DeepHit model, i.e., the encoder, is made of a joint MLP block. The decoder of the model is made of MLP blocks, each specific to one event. The output of each case-specific block is a discrete hazard PDF for each event. The last layer of each case-specific block contains \(N\)  units, where each unit generates the likelihood for one timestep (\(e.g.\)  one hour or one month) of the hazard PDF over the prediction horizon. A major drawback of this method for generating probability distributions is that the predictions of the output layer could arbitrarily vary from one unit to the next. This causes the overall generated PDFs to be extremely noisy. Moreover, depending on the number of time steps in the prediction horizon, the number of trainable parameters in the output layer could become too high and may cause overfitting when training the model.
